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R Data Science Essentials
R Data Science Essentials

R Data Science Essentials: R Data Science Essentials

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Profile Icon Koushik Profile Icon Kumar Ravindran
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$31.19 $38.99
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Paperback Jan 2016 154 pages 1st Edition
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R Data Science Essentials

Chapter 2. Exploratory Data Analysis

Exploratory data analysis is a very important topic in the field of data analysis. It is an approach of analyzing the data and summarizing the main characteristics of the dataset. The main objective of exploratory data analysis is to check various hypotheses in order to get a better understanding about the dataset.

Exploratory data analysis includes many statistical techniques and visual and nonvisual analysis. When your study has to be communicated with peers as well as with other audience with non-data science backgrounds, it is advisable to use a lot of visual techniques that help in better communications.

Some of the expectations out of exploratory data analysis are getting insights out of the data, extracting the important variables in the dataset (depending on the problem to be solved), identifying the outliers in the data, and getting results of various testing hypotheses. These results play a very important role in how to solve the business...

The Titanic dataset

In this chapter, let's use the Titanic dataset, which is available on the Internet and also hosted on GitHub, to implement various techniques. Place the dataset in the current working directory in R; before this, first set the working directory accordingly using the setwd() command. The setwd() function is used to specify the location that should be considered as the current working directory. Now, read the data using the read.csv function and store it in a data frame. In this book, we have named the data frame tdata. The various details that are present in the dataset, which is hosted on GitHub, are as follows:

tdata<- read.csv("titanic.csv")
names(tdata)

The output of the preceding command is as follows:

The Titanic dataset

These are the various columns captured in the dataset. The explanation of these variables is given. For more detailed understanding about the dataset, visit https://www.kaggle.com/c/titanic/data. We have used the file named train.csv for our learning...

Descriptive statistics

Descriptive statistics is a method of summarizing a dataset quantitatively. These summaries can be simple quantitative statements about the data or a visual representation sufficient enough to be part of the initial description about the dataset.

To get a basic understanding about the dataset, we can use the built-in function summary. This function quickly scans the dataset and provides the following information about the dataset. This will really help in getting a first-cut understanding about the data. This will be useful for numerical as well as categorical data.

summary(tdata)

The output is as follows:

Descriptive statistics

The summary function provides us with a high-level detail about the variables in the dataset. In order to know more about the dataset such as the missing values, distribution of numerical variables, and distinct values of categorical variables, we need to use an additional package called Hmisc. (The implementation of this is given here.) The package can be installed...

Inferential statistics

We have seen a sufficient number of descriptive statistics techniques. Now, let's check some of the inferential statistics techniques. Inferential statistics is used to infer properties about the dataset.

First, let's start with the simple mean and check what should be the range if the mean has to fall under the confidence interval of 95%. In order to get the confidence interval for the mean, we need to load the lsr package; if the package is not already installed, you need to install it using the install.packages function and then the ciMean function to get the desired result:

library(lsr)
ciMean(tdata$Fare)

The following is the output of the preceding command:

Inferential statistics

The ciMean function gives us an overall view on the confidence interval of the Fare variable. However, to see how different it is between male and female, we can use the aggregate function:

aggregate( tdata$Fare ~ tdata$Sex, tdata, ciMean )

The output of the preceding is as follows:

Inferential statistics

From the preceding...

Univariate analysis

Univariate analysis is the simplest form of analysis, where we consider only one variable at a time and understand the data. Some of the measures have already been covered in descriptive statistics such as the mean and median of the data.

We will perform one more univariate analysis: the distribution of the data. We will consider the age of the people who had travelled in the Titanic, and we will find out how many people were there in the different age groups:

age <- na.omit(tdata$Age)

First, we read the data to the age data frame by excluding the cases where the age was not present. As we want to get the distribution on a fixed range, we first get the age of the youngest as well as the oldest person who travelled on the ship from the available dataset using the seq function. We set the starting value as 0 and the last value as 80; we also set the interval as 10:

range(age)
breaks = seq(0, 80, by=10)

We created the intervals and stored them in the variable breaks. Using...

Bivariate analysis

In this section, we will cover bivariate analysis to understand the combined effect of two variables as well as the effect of one variable on the other variable. In any real-life example, there will be multiple variables dependent on each other. Hence, this analysis will be useful in getting an understanding about these cases.

The best method to get a quick understanding about two variables is the scatter plot. This visual representation gives us a clear idea about the impact of one variable on the other variable. We can use the same ggplot function to plot the scatter plot. We will plot the scatter chart to get the relationship between the Age and Fare variables:

ggplot(tdata, aes(x=Fare, y=Age)) +
geom_point(shape=1) +    
geom_smooth(method=lm)
ggsave(file="scatter-plot.png", dpi=500)

In the preceding case, we are plotting the relationship between these two variables along with the scatter plot, using the geom_smooth parameter, which plots an additional linear...

The Titanic dataset


In this chapter, let's use the Titanic dataset, which is available on the Internet and also hosted on GitHub, to implement various techniques. Place the dataset in the current working directory in R; before this, first set the working directory accordingly using the setwd() command. The setwd() function is used to specify the location that should be considered as the current working directory. Now, read the data using the read.csv function and store it in a data frame. In this book, we have named the data frame tdata. The various details that are present in the dataset, which is hosted on GitHub, are as follows:

tdata<- read.csv("titanic.csv")
names(tdata)

The output of the preceding command is as follows:

These are the various columns captured in the dataset. The explanation of these variables is given. For more detailed understanding about the dataset, visit https://www.kaggle.com/c/titanic/data. We have used the file named train.csv for our learning purpose.

The variable...

Descriptive statistics


Descriptive statistics is a method of summarizing a dataset quantitatively. These summaries can be simple quantitative statements about the data or a visual representation sufficient enough to be part of the initial description about the dataset.

To get a basic understanding about the dataset, we can use the built-in function summary. This function quickly scans the dataset and provides the following information about the dataset. This will really help in getting a first-cut understanding about the data. This will be useful for numerical as well as categorical data.

summary(tdata)

The output is as follows:

The summary function provides us with a high-level detail about the variables in the dataset. In order to know more about the dataset such as the missing values, distribution of numerical variables, and distinct values of categorical variables, we need to use an additional package called Hmisc. (The implementation of this is given here.) The package can be installed...

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Key benefits

  • *Become a pro at making stunning visualizations and dashboards quickly and without hassle
  • *For better decision making in business, apply the R programming language with the help of useful statistical techniques.
  • *From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patterns

Description

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.

Who is this book for?

If you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you.

What you will learn

  • *Perform data preprocessing and basic operations on data
  • *Implement visual and non-visual implementation data exploration techniques
  • *Mine patterns from data using affinity and sequential analysis
  • *Use different clustering algorithms and visualize them
  • *Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm
  • *Extract patterns through visualization and build a forecasting algorithm
  • *Build a recommendation engine using different collaborative filtering algorithms
  • *Make a stunning visualization and dashboard using ggplot and R shiny
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Table of Contents

9 Chapters
1. Getting Started with R Chevron down icon Chevron up icon
2. Exploratory Data Analysis Chevron down icon Chevron up icon
3. Pattern Discovery Chevron down icon Chevron up icon
4. Segmentation Using Clustering Chevron down icon Chevron up icon
5. Developing Regression Models Chevron down icon Chevron up icon
6. Time Series Forecasting Chevron down icon Chevron up icon
7. Recommendation Engine Chevron down icon Chevron up icon
8. Communicating Data Analysis Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(3 Ratings)
5 star 0%
4 star 33.3%
3 star 33.3%
2 star 33.3%
1 star 0%
J Riks May 25, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Good for the essntials however some mistakes in the codes. Without R knowledge not a reccomended book for you at the moment. When experienced with R you should be able to work it out.
Amazon Verified review Amazon
sker Apr 07, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
This book had good information and was fine for getting started, but it lacked depth.
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Amazon user Oct 14, 2016
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Some of the codes are flawed/not working.
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